{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import iris\n",
    "import iris.plot as iplt\n",
    "import iris.quickplot as qplt\n",
    "import iris.pandas as ip\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "\n",
    "import sys\n",
    "import os\n",
    "sys.path.append(\"../../../Scripts/\")\n",
    "import preprocessing.retrieve_temp_data\n",
    "import utils.temporal_utils\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## retrieve weather data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# file paths \n",
    "mtl_temp_prec_2016_path = \"../../../Data/supplementary_data/weather/mtl_temp_prec_2016.nc\"\n",
    "mtl_temp_prec_2017_path = \"../../../Data/supplementary_data/weather/mtl_temp_prec_2017.nc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file already exists\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(mtl_temp_prec_2016_path):\n",
    "    # use the output filename: '../../../Data/supplementary_data/weather/mtl_temp_prec_2016'\n",
    "    preprocessing.retrieve_temp_data.extract_temp_data()\n",
    "else:\n",
    "    print(\"file already exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file already exists\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(mtl_temp_prec_2017_path):\n",
    "    # NOTE: use the output filename: '../../../Data/supplementary_data/weather/mtl_temp_prec_2017'\n",
    "    preprocessing.retrieve_temp_data.extract_temp_data()\n",
    "else:\n",
    "    print(\"file already exists\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## clean and merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: 2 metre temperature / (K)           (time: 2928; latitude: 1; longitude: 1)\n",
      "1: Total precipitation / (m)           (time: 2928; latitude: 1; longitude: 1)\n",
      "\n",
      " 2017: \n",
      " 0: 2 metre temperature / (K)           (time: 1464; latitude: 1; longitude: 1)\n",
      "1: Total precipitation / (m)           (time: 1464; latitude: 1; longitude: 1)\n"
     ]
    }
   ],
   "source": [
    "cubes_16 = iris.load(mtl_temp_prec_2016_path)\n",
    "print(cubes_16)\n",
    "mtl_temp_16 = cubes_16[0]\n",
    "mtl_prec_16 = cubes_16[1]\n",
    "\n",
    "cubes_17 = iris.load(mtl_temp_prec_2017_path)\n",
    "print(\"\\n 2017: \\n\",cubes_17)\n",
    "mtl_temp_17 = cubes_17[0]\n",
    "mtl_prec_17 = cubes_17[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[DimCoord(array([1022688, 1022689, 1022690, ..., 1025613, 1025614, 1025615],\n",
       "       dtype=int32), standard_name='time', units=Unit('hours since 1900-01-01 00:00:00.0', calendar='gregorian'), long_name='time', var_name='time'),\n",
       " DimCoord(array([45.], dtype=float32), standard_name='latitude', units=Unit('degrees'), long_name='latitude', var_name='latitude'),\n",
       " DimCoord(array([-73.], dtype=float32), standard_name='longitude', units=Unit('degrees'), long_name='longitude', var_name='longitude')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view coords of time\n",
    "mtl_temp_16.coords()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_array(\n",
       "  data=[[[292.70101968]],\n",
       "\n",
       "        [[292.46779795]],\n",
       "\n",
       "        [[291.68311213]],\n",
       "\n",
       "        [[291.44365035]],\n",
       "\n",
       "        [[291.52711097]]],\n",
       "  mask=False,\n",
       "  fill_value=1e+20)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view data -> it is in Kelvin\n",
    "mtl_temp_16.data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1022688\n",
      "1031448\n"
     ]
    }
   ],
   "source": [
    "# first date\n",
    "print(mtl_temp_16.coords('time')[0][0].points[0])\n",
    "print(mtl_temp_17.coords('time')[0][0].points[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## subset by dates of study (determined by hand)\n",
    "# check if cube has already been subset by date\n",
    "if not mtl_temp_16.coords('time')[0][0].points[0] > 1022688:\n",
    "    mtl_temp_16 = mtl_temp_16[24:2520]\n",
    "    mtl_prec_16 = mtl_prec_16[24:2520]\n",
    "else:\n",
    "    print(\"2016 already subset!\")\n",
    "    \n",
    "if not mtl_temp_17.coords('time')[0][0].points[0] > 1031448:\n",
    "    mtl_temp_17 = mtl_temp_17[408:1128]\n",
    "    mtl_prec_17 = mtl_prec_17[408:1128]\n",
    "else:\n",
    "    print(\"2017 already subset!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  a.iris {\n",
       "      text-decoration: none !important;\n",
       "  }\n",
       "  table.iris {\n",
       "      white-space: pre;\n",
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       "      font-size: 1.05em;\n",
       "      min-width: 50px;\n",
       "      max-width: 125px;\n",
       "  }\n",
       "  tr.iris :first-child {\n",
       "      border-right: 1px solid #9c9c9c !important;\n",
       "  }\n",
       "  td.iris-title {\n",
       "      background: #d5dcdf;\n",
       "      border-top: 1px solid #9c9c9c;\n",
       "      font-weight: bold;\n",
       "  }\n",
       "  .iris-word-cell {\n",
       "      text-align: left !important;\n",
       "      white-space: pre;\n",
       "  }\n",
       "  .iris-subheading-cell {\n",
       "      padding-left: 2em !important;\n",
       "  }\n",
       "  .iris-inclusion-cell {\n",
       "      padding-right: 1em !important;\n",
       "  }\n",
       "  .iris-panel-body {\n",
       "      padding-top: 0px;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      padding-left: 3em;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      margin-top: 7px;\n",
       "  }\n",
       "</style>\n",
       "<table class=\"iris\" id=\"4704290128\">\n",
       "    <tr class=\"iris\">\n",
       "<th class=\"iris iris-word-cell\">Total Precipitation (m)</th>\n",
       "<th class=\"iris iris-word-cell\">latitude</th>\n",
       "<th class=\"iris iris-word-cell\">longitude</th>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "<td class=\"iris-word-cell iris-subheading-cell\">Shape</td>\n",
       "<td class=\"iris iris-inclusion-cell\">1</td>\n",
       "<td class=\"iris iris-inclusion-cell\">1</td>\n",
       "</td>\n",
       "    <tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Dimension coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlatitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlongitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Scalar coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttime</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"2\">2016-09-02 00:00:00</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Attributes</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tConventions</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"2\">CF-1.6</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\thistory</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"2\">2019-10-20 13:47:16 GMT by grib_to_netcdf-2.14.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf...</td>\n",
       "</tr>\n",
       "</table>\n",
       "        "
      ],
      "text/plain": [
       "<iris 'Cube' of Total precipitation / (m) (latitude: 1; longitude: 1)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mtl_prec_16[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  a.iris {\n",
       "      text-decoration: none !important;\n",
       "  }\n",
       "  table.iris {\n",
       "      white-space: pre;\n",
       "      border: 1px solid;\n",
       "      border-color: #9c9c9c;\n",
       "      font-family: monaco, monospace;\n",
       "  }\n",
       "  th.iris {\n",
       "      background: #303f3f;\n",
       "      color: #e0e0e0;\n",
       "      border-left: 1px solid;\n",
       "      border-color: #9c9c9c;\n",
       "      font-size: 1.05em;\n",
       "      min-width: 50px;\n",
       "      max-width: 125px;\n",
       "  }\n",
       "  tr.iris :first-child {\n",
       "      border-right: 1px solid #9c9c9c !important;\n",
       "  }\n",
       "  td.iris-title {\n",
       "      background: #d5dcdf;\n",
       "      border-top: 1px solid #9c9c9c;\n",
       "      font-weight: bold;\n",
       "  }\n",
       "  .iris-word-cell {\n",
       "      text-align: left !important;\n",
       "      white-space: pre;\n",
       "  }\n",
       "  .iris-subheading-cell {\n",
       "      padding-left: 2em !important;\n",
       "  }\n",
       "  .iris-inclusion-cell {\n",
       "      padding-right: 1em !important;\n",
       "  }\n",
       "  .iris-panel-body {\n",
       "      padding-top: 0px;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      padding-left: 3em;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      margin-top: 7px;\n",
       "  }\n",
       "</style>\n",
       "<table class=\"iris\" id=\"4704135880\">\n",
       "    <tr class=\"iris\">\n",
       "<th class=\"iris iris-word-cell\">Total Precipitation (m)</th>\n",
       "<th class=\"iris iris-word-cell\">latitude</th>\n",
       "<th class=\"iris iris-word-cell\">longitude</th>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "<td class=\"iris-word-cell iris-subheading-cell\">Shape</td>\n",
       "<td class=\"iris iris-inclusion-cell\">1</td>\n",
       "<td class=\"iris iris-inclusion-cell\">1</td>\n",
       "</td>\n",
       "    <tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Dimension coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlatitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlongitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Scalar coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttime</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"2\">2017-09-18 00:00:00</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Attributes</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tConventions</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"2\">CF-1.6</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\thistory</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"2\">2019-10-20 14:24:13 GMT by grib_to_netcdf-2.14.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf...</td>\n",
       "</tr>\n",
       "</table>\n",
       "        "
      ],
      "text/plain": [
       "<iris 'Cube' of Total precipitation / (m) (latitude: 1; longitude: 1)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mtl_prec_17[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## convert temperature to Celsius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert from K to C.\n",
    "if mtl_temp_16.data[0][0][0] > 100:\n",
    "    mtl_temp_16.data = mtl_temp_16.data - 273.15\n",
    "else:\n",
    "    print(\"2016 already converted\")\n",
    "    \n",
    "if mtl_temp_17.data[0][0][0] > 100:\n",
    "    mtl_temp_17.data = mtl_temp_17.data - 273.15 \n",
    "else:\n",
    "    print(\"2017 already converted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 720)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax, ax2) = plt.subplots(1,2, figsize=(10,8), sharey=True)\n",
    "ax.plot([i[0][0] for i in mtl_temp_16.data.data], alpha=.75)\n",
    "ax1 = ax.twinx()\n",
    "ax1.plot([i[0][0] for i in mtl_prec_16.data.data], color='red', alpha=.75)\n",
    "ax.set_title(\"2016\")\n",
    "ax.set_xlim(0,720)\n",
    "\n",
    "ax2.plot([i[0][0] for i in mtl_temp_17.data.data], alpha=.75)\n",
    "ax3 = ax2.twinx()\n",
    "ax3.plot([i[0][0] for i in mtl_prec_17.data.data], color='red', alpha=.75)\n",
    "ax2.set_title(\"2017\")\n",
    "ax2.set_xlim(0,720)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2016 until December')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots(1, figsize=(10,8), sharey=True)\n",
    "ax.plot([i[0][0] for i in mtl_temp_16.data.data], alpha=.75)\n",
    "ax1 = ax.twinx()\n",
    "ax1.plot([i[0][0] for i in mtl_prec_16.data.data], color='red', alpha=.75)\n",
    "ax.set_title(\"2016 until December\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## add to MTL_Trajet data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_weather_df(mtl_temp, mtl_prec):\n",
    "    first_time_hours = int(mtl_temp.coords()[0][0].points[0])\n",
    "    last_time_hours = int(mtl_temp.coords()[0][-1].points[0])\n",
    "    last_time_point = datetime.datetime(1900,1,1,0) + datetime.timedelta(hours=last_time_hours)\n",
    "    total_hours = len(mtl_temp.coords()[0].points)\n",
    "    \n",
    "    # get all hours between the start and end dates\n",
    "    date_list = [last_time_point - datetime.timedelta(hours=x) for x in range(total_hours)]\n",
    "    date_list.reverse()\n",
    "    \n",
    "    precs = [i[0][0] for i in mtl_prec.data.data]\n",
    "    temps = [i[0][0] for i in mtl_temp.data.data]\n",
    "    df_dict = {\"precipitation\" : precs, \"temperature\" : temps}\n",
    "    df = pd.DataFrame.from_dict(df_dict)\n",
    "    df['dt'] = date_list\n",
    "    return df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# make weather dataframes and append them to each other\n",
    "weather_16 = make_weather_df(mtl_temp=mtl_temp_16, mtl_prec=mtl_prec_16)\n",
    "weather_17 = make_weather_df(mtl_temp=mtl_temp_17, mtl_prec=mtl_prec_17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00172</td>\n",
       "      <td>18.253090</td>\n",
       "      <td>2016-09-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.749206</td>\n",
       "      <td>2016-09-02 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.533925</td>\n",
       "      <td>2016-09-02 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.394304</td>\n",
       "      <td>2016-09-02 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.090101</td>\n",
       "      <td>2016-09-02 04:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precipitation  temperature                  dt\n",
       "0        0.00172    18.253090 2016-09-02 00:00:00\n",
       "1        0.00000    17.749206 2016-09-02 01:00:00\n",
       "2        0.00000    17.533925 2016-09-02 02:00:00\n",
       "3        0.00000    17.394304 2016-09-02 03:00:00\n",
       "4        0.00000    17.090101 2016-09-02 04:00:00"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_16.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>temperature</th>\n",
       "      <th>dt</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>21.655905</td>\n",
       "      <td>2017-09-18 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>2017-09-18 01:00:00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.182104</td>\n",
       "      <td>2017-09-18 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.452417</td>\n",
       "      <td>2017-09-18 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.951944</td>\n",
       "      <td>2017-09-18 04:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precipitation  temperature                  dt\n",
       "0       0.000004    21.655905 2017-09-18 00:00:00\n",
       "1       0.000000    20.891177 2017-09-18 01:00:00\n",
       "2       0.000000    20.182104 2017-09-18 02:00:00\n",
       "3       0.000000    19.452417 2017-09-18 03:00:00\n",
       "4       0.000000    18.951944 2017-09-18 04:00:00"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_17.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data = weather_16.append(weather_17).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data.to_csv(\"../../../Data/supplementary_data/weather/mtl_temp_prec.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Note \n",
    "For appending to data, see 'all_preprocessing' notebook. \n",
    "\n",
    "Code used to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data = pd.read_csv(\"../../../Data/supplementary_data/weather/mtl_temp_prec.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>dt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00172</td>\n",
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       "      <td>2016-09-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.749206</td>\n",
       "      <td>2016-09-02 01:00:00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.533925</td>\n",
       "      <td>2016-09-02 02:00:00</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.394304</td>\n",
       "      <td>2016-09-02 03:00:00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.090101</td>\n",
       "      <td>2016-09-02 04:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precipitation  temperature                   dt\n",
       "0        0.00172    18.253090  2016-09-02 00:00:00\n",
       "1        0.00000    17.749206  2016-09-02 01:00:00\n",
       "2        0.00000    17.533925  2016-09-02 02:00:00\n",
       "3        0.00000    17.394304  2016-09-02 03:00:00\n",
       "4        0.00000    17.090101  2016-09-02 04:00:00"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data['dt'] = weather_data['dt'].apply(pd.to_datetime)\n",
    "weather_data = weather_data.set_index('dt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_weather_subset(row, weather_df):\n",
    "    assert type(weather_df.index) == pd.core.indexes.datetimes.DatetimeIndex, (\"the weather data needs a datetime index\")\n",
    "    \n",
    "    start = row['starttime']\n",
    "    end = row['endtime']\n",
    "    subset_weather = weather_df.loc[start.strftime(\"%Y-%m-%d %H:%M:%S\"):end.strftime(\"%Y-%m-%d %H:%M:%S\")]\n",
    "    if len(subset_weather) < 1:\n",
    "        start = start - datetime.timedelta(hours=1)\n",
    "        subset_weather = weather_df.loc[start.strftime(\"%Y-%m-%d %H:%M:%S\"):end.strftime(\"%Y-%m-%d %H:%M:%S\")]\n",
    "    return subset_weather\n",
    "\n",
    "def calc_mean_weather(subset):\n",
    "#     print(len(subset), \"hours worth of weather\")\n",
    "    return subset.precipitation.mean(), subset.temperature.mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in data as example\n",
    "path_2016 = \"../../../Data/mtl_trajet/mtl_trajet_2016_translated.shp\"\n",
    "gdf_2016 = gpd.read_file(path_2016)\n",
    "gdf_2016 = utils.temporal_utils.convert_timestamps_to_datetime(gdf_2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "## apply average weather for trip to each row\n",
    "## takes X mins\n",
    "gdf_2016['av_weather'] = gdf_2016.apply(lambda row: calc_mean_weather(extract_weather_subset(row, weather_data)), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_prec = gdf_2016.av_weather.apply(pd.Series)\n",
    "temp_prec.columns = ['precip','temperature']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_2016 = pd.concat([gdf_2016,temp_prec], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_trip</th>\n",
       "      <th>avg_speed</th>\n",
       "      <th>duration</th>\n",
       "      <th>mode</th>\n",
       "      <th>purpose</th>\n",
       "      <th>n_coord</th>\n",
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       "      <th>starttime</th>\n",
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       "      <th>precip</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1724206</td>\n",
       "      <td>4.4</td>\n",
       "      <td>460</td>\n",
       "      <td>Ã pied</td>\n",
       "      <td>Retour Ã  la maison</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1724208</td>\n",
       "      <td>10.7</td>\n",
       "      <td>2146</td>\n",
       "      <td>Autre combinaison</td>\n",
       "      <td>Travail</td>\n",
       "      <td>120</td>\n",
       "      <td>[{\"id\": 1140016, \"source\": \"geobase_mtl\"}, {\"i...</td>\n",
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       "      <th>2</th>\n",
       "      <td>1889461</td>\n",
       "      <td>15.4</td>\n",
       "      <td>447</td>\n",
       "      <td>Transport Collectif</td>\n",
       "      <td>Loisir</td>\n",
       "      <td>36</td>\n",
       "      <td>[{\"id\": 1390715, \"source\": \"geobase_mtl\"}, {\"i...</td>\n",
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       "      <td>0.000134</td>\n",
       "      <td>25.844886</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1724219</td>\n",
       "      <td>16.8</td>\n",
       "      <td>591</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Retour Ã  la maison</td>\n",
       "      <td>45</td>\n",
       "      <td>[{\"id\": 1210250, \"source\": \"geobase_mtl\"}, {\"i...</td>\n",
       "      <td>2016-09-08 21:41:37-04:00</td>\n",
       "      <td>2016-09-08 21:51:28-04:00</td>\n",
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       "      <td>(0.00024012980236755996, 25.38936301243564)</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>25.389363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2071985</td>\n",
       "      <td>6.9</td>\n",
       "      <td>279</td>\n",
       "      <td>Autre combinaison</td>\n",
       "      <td>DÃ©poser / Ramasser</td>\n",
       "      <td>12</td>\n",
       "      <td>[{\"id\": 1140287, \"source\": \"geobase_mtl\"}, {\"i...</td>\n",
       "      <td>2016-09-09 16:49:12-04:00</td>\n",
       "      <td>2016-09-09 16:53:51-04:00</td>\n",
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       "      <td>23.612510</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   id_trip  avg_speed  duration                 mode              purpose  \\\n",
       "0  1724206        4.4       460              Ã pied  Retour Ã  la maison   \n",
       "1  1724208       10.7      2146    Autre combinaison              Travail   \n",
       "2  1889461       15.4       447  Transport Collectif               Loisir   \n",
       "3  1724219       16.8       591           Automobile  Retour Ã  la maison   \n",
       "4  2071985        6.9       279    Autre combinaison  DÃ©poser / Ramasser   \n",
       "\n",
       "   n_coord                                           segments  \\\n",
       "0       12  [{\"id\": 1150192, \"source\": \"geobase_mtl\"}, {\"i...   \n",
       "1      120  [{\"id\": 1140016, \"source\": \"geobase_mtl\"}, {\"i...   \n",
       "2       36  [{\"id\": 1390715, \"source\": \"geobase_mtl\"}, {\"i...   \n",
       "3       45  [{\"id\": 1210250, \"source\": \"geobase_mtl\"}, {\"i...   \n",
       "4       12  [{\"id\": 1140287, \"source\": \"geobase_mtl\"}, {\"i...   \n",
       "\n",
       "                   starttime                    endtime  \\\n",
       "0  2016-09-07 20:37:26-04:00  2016-09-07 20:45:06-04:00   \n",
       "1  2016-09-08 07:43:23-04:00  2016-09-08 08:19:09-04:00   \n",
       "2  2016-09-08 19:46:14-04:00  2016-09-08 19:53:41-04:00   \n",
       "3  2016-09-08 21:41:37-04:00  2016-09-08 21:51:28-04:00   \n",
       "4  2016-09-09 16:49:12-04:00  2016-09-09 16:53:51-04:00   \n",
       "\n",
       "                                            geometry  \\\n",
       "0  LINESTRING (-73.5983784907 45.5364060323, -73....   \n",
       "1  LINESTRING (-73.6057240345 45.5332354867, -73....   \n",
       "2  LINESTRING (-73.55313767040001 45.5261139512, ...   \n",
       "3  LINESTRING (-73.5739219686 45.5336004969, -73....   \n",
       "4  LINESTRING (-73.5977874175 45.5317833425, -73....   \n",
       "\n",
       "                                     av_weather    precip  temperature  \n",
       "0  (1.5039444824678503e-06, 28.012522481572827)  0.000002    28.012522  \n",
       "1                     (0.0, 20.969070204942227)  0.000000    20.969070  \n",
       "2    (0.0001343523737672367, 25.84488639333847)  0.000134    25.844886  \n",
       "3   (0.00024012980236755996, 25.38936301243564)  0.000240    25.389363  \n",
       "4    (0.001389143386973931, 23.612509824599048)  0.001389    23.612510  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
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    }
   ],
   "source": [
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   ]
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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